{
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  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.6",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "name": "insurance_cross_sell.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WAGvd4aTBYEe"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/009/solution/insurance_cross_sell.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "kcCNBhh26ws-"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import roc_auc_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "trusted": true,
        "id": "4Ld7_68i6wto"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set_style(\"darkgrid\")\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "RcMrDhBL6wt8",
        "outputId": "a7b26501-96c2-4506-c7b0-5843ab1d1b0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "DATA_PATH='https://raw.githubusercontent.com/gimseng/99-ML-Learning-Projects/master/009/data/'\n",
        "\n",
        "trainData = pd.read_csv(DATA_PATH+'train.csv')\n",
        "trainData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Driving_License</th>\n",
              "      <th>Region_Code</th>\n",
              "      <th>Previously_Insured</th>\n",
              "      <th>Vehicle_Age</th>\n",
              "      <th>Vehicle_Damage</th>\n",
              "      <th>Annual_Premium</th>\n",
              "      <th>Policy_Sales_Channel</th>\n",
              "      <th>Vintage</th>\n",
              "      <th>Response</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>&gt; 2 Years</td>\n",
              "      <td>Yes</td>\n",
              "      <td>40454.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1-2 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>33536.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>183</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>&gt; 2 Years</td>\n",
              "      <td>Yes</td>\n",
              "      <td>38294.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>28619.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>27496.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  Gender  Age  ...  Policy_Sales_Channel  Vintage  Response\n",
              "0   1    Male   44  ...                  26.0      217         1\n",
              "1   2    Male   76  ...                  26.0      183         0\n",
              "2   3    Male   47  ...                  26.0       27         1\n",
              "3   4    Male   21  ...                 152.0      203         0\n",
              "4   5  Female   29  ...                 152.0       39         0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8LiwSXnJ6wur",
        "outputId": "e1d88435-9d3e-49b0-a0a5-ecf936b5dd41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "testData = pd.read_csv(DATA_PATH+'test.csv')\n",
        "testData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Driving_License</th>\n",
              "      <th>Region_Code</th>\n",
              "      <th>Previously_Insured</th>\n",
              "      <th>Vehicle_Age</th>\n",
              "      <th>Vehicle_Damage</th>\n",
              "      <th>Annual_Premium</th>\n",
              "      <th>Policy_Sales_Channel</th>\n",
              "      <th>Vintage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>381110</td>\n",
              "      <td>Male</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>35786.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>381111</td>\n",
              "      <td>Male</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1-2 Year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>33762.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>381112</td>\n",
              "      <td>Male</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1-2 Year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>40050.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>381113</td>\n",
              "      <td>Male</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>37356.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>381114</td>\n",
              "      <td>Male</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>59097.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>297</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       id Gender  Age  ...  Annual_Premium  Policy_Sales_Channel  Vintage\n",
              "0  381110   Male   25  ...         35786.0                 152.0       53\n",
              "1  381111   Male   40  ...         33762.0                   7.0      111\n",
              "2  381112   Male   47  ...         40050.0                 124.0      199\n",
              "3  381113   Male   24  ...         37356.0                 152.0      187\n",
              "4  381114   Male   27  ...         59097.0                 152.0      297\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "hw66ZUsN6wu4",
        "outputId": "de7e49c0-98df-49b5-abe9-461010757f0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "trainData = trainData.drop(['id'], axis = 1)\n",
        "trainData.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 381109 entries, 0 to 381108\n",
            "Data columns (total 11 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   Gender                381109 non-null  object \n",
            " 1   Age                   381109 non-null  int64  \n",
            " 2   Driving_License       381109 non-null  int64  \n",
            " 3   Region_Code           381109 non-null  float64\n",
            " 4   Previously_Insured    381109 non-null  int64  \n",
            " 5   Vehicle_Age           381109 non-null  object \n",
            " 6   Vehicle_Damage        381109 non-null  object \n",
            " 7   Annual_Premium        381109 non-null  float64\n",
            " 8   Policy_Sales_Channel  381109 non-null  float64\n",
            " 9   Vintage               381109 non-null  int64  \n",
            " 10  Response              381109 non-null  int64  \n",
            "dtypes: float64(3), int64(5), object(3)\n",
            "memory usage: 32.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "dIe4Vd3W6wu8",
        "outputId": "01d90c53-452b-4732-a055-c76fa24db117",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "testData = testData.drop(['id'], axis = 1)\n",
        "testData.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 127037 entries, 0 to 127036\n",
            "Data columns (total 10 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   Gender                127037 non-null  object \n",
            " 1   Age                   127037 non-null  int64  \n",
            " 2   Driving_License       127037 non-null  int64  \n",
            " 3   Region_Code           127037 non-null  float64\n",
            " 4   Previously_Insured    127037 non-null  int64  \n",
            " 5   Vehicle_Age           127037 non-null  object \n",
            " 6   Vehicle_Damage        127037 non-null  object \n",
            " 7   Annual_Premium        127037 non-null  float64\n",
            " 8   Policy_Sales_Channel  127037 non-null  float64\n",
            " 9   Vintage               127037 non-null  int64  \n",
            "dtypes: float64(3), int64(4), object(3)\n",
            "memory usage: 9.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "_3s8lcZt6wu_",
        "outputId": "734086d1-a2e3-4481-ab17-f3f31f97ae3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "#Checking for null values\n",
        "print(trainData.isnull().sum())\n",
        "print(\" \")\n",
        "print(testData.isnull().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gender                  0\n",
            "Age                     0\n",
            "Driving_License         0\n",
            "Region_Code             0\n",
            "Previously_Insured      0\n",
            "Vehicle_Age             0\n",
            "Vehicle_Damage          0\n",
            "Annual_Premium          0\n",
            "Policy_Sales_Channel    0\n",
            "Vintage                 0\n",
            "Response                0\n",
            "dtype: int64\n",
            " \n",
            "Gender                  0\n",
            "Age                     0\n",
            "Driving_License         0\n",
            "Region_Code             0\n",
            "Previously_Insured      0\n",
            "Vehicle_Age             0\n",
            "Vehicle_Damage          0\n",
            "Annual_Premium          0\n",
            "Policy_Sales_Channel    0\n",
            "Vintage                 0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIPtmDIJ6wvN"
      },
      "source": [
        "No value is null"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ibt_A8jd6wvO",
        "outputId": "13c92630-e3a7-4750-e72c-4e8594cfa6e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        "trainData[\"Response\"].value_counts().plot.pie(colormap = \"summer\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fecf6b9dda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "tVqlvwAc6wvQ"
      },
      "source": [
        "#Getting data in numerical entities\n",
        "\n",
        "###Gender\n",
        "def Gender(gen):\n",
        "    if gen == 'Female':\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "trainData['Gender'] = trainData['Gender'].apply(Gender)\n",
        "testData['Gender'] = testData['Gender'].apply(Gender)\n",
        "\n",
        "###Vehicle Age\n",
        "def VehicleAge(age):\n",
        "    if age == '< 1 Year':\n",
        "        return 0\n",
        "    elif age == '1-2 Year':\n",
        "        return 1\n",
        "    else:\n",
        "        return 2\n",
        "trainData['Vehicle_Age'] = trainData['Vehicle_Age'].apply(VehicleAge)    \n",
        "testData['Vehicle_Age'] = testData['Vehicle_Age'].apply(VehicleAge)    \n",
        "\n",
        "###Vehicle Damage\n",
        "def VehicleDamage(damage):\n",
        "    if damage == 'Yes':\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "trainData['Vehicle_Damage'] = trainData['Vehicle_Damage'].apply(VehicleDamage)\n",
        "testData['Vehicle_Damage'] = testData['Vehicle_Damage'].apply(VehicleDamage)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "fhCHPPRU6wvT",
        "outputId": "10cc9979-8254-4d31-aed9-3f87102ca20e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "trainData = trainData.drop(['Driving_License'], axis = 1)\n",
        "trainData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Region_Code</th>\n",
              "      <th>Previously_Insured</th>\n",
              "      <th>Vehicle_Age</th>\n",
              "      <th>Vehicle_Damage</th>\n",
              "      <th>Annual_Premium</th>\n",
              "      <th>Policy_Sales_Channel</th>\n",
              "      <th>Vintage</th>\n",
              "      <th>Response</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>40454.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>33536.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>183</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>38294.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28619.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27496.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Age  Region_Code  ...  Policy_Sales_Channel  Vintage  Response\n",
              "0       0   44         28.0  ...                  26.0      217         1\n",
              "1       0   76          3.0  ...                  26.0      183         0\n",
              "2       0   47         28.0  ...                  26.0       27         1\n",
              "3       0   21         11.0  ...                 152.0      203         0\n",
              "4       1   29         41.0  ...                 152.0       39         0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Vvus2bDb6wvV",
        "outputId": "53fcd811-4b7c-49a6-b09e-4d1631ccf224",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "testData= testData.drop(['Driving_License'], axis=1)\n",
        "testData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Region_Code</th>\n",
              "      <th>Previously_Insured</th>\n",
              "      <th>Vehicle_Age</th>\n",
              "      <th>Vehicle_Damage</th>\n",
              "      <th>Annual_Premium</th>\n",
              "      <th>Policy_Sales_Channel</th>\n",
              "      <th>Vintage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35786.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>33762.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>40050.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>37356.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>59097.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>297</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Age  Region_Code  ...  Annual_Premium  Policy_Sales_Channel  Vintage\n",
              "0       0   25         11.0  ...         35786.0                 152.0       53\n",
              "1       0   40         28.0  ...         33762.0                   7.0      111\n",
              "2       0   47         28.0  ...         40050.0                 124.0      199\n",
              "3       0   24         27.0  ...         37356.0                 152.0      187\n",
              "4       0   27         28.0  ...         59097.0                 152.0      297\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "I81knary6wvX",
        "outputId": "2f98a440-601a-4aa9-ba28-fe3aae56ef47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        }
      },
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(trainData.corr(), annot=True,vmin=-1, vmax=1, center= 0, cmap= 'coolwarm', linewidths=3, linecolor='black')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fecf6bfc4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "WEMvDu0M6wve"
      },
      "source": [
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV, StratifiedKFold, KFold, GridSearchCV\n",
        "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score,recall_score\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler, RobustScaler, Normalizer\n",
        "from sklearn.utils.multiclass import type_of_target\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "from sklearn import preprocessing"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "_ZYlST0W6wvh"
      },
      "source": [
        "numCols = ['Age','Region_Code', 'Policy_Sales_Channel', 'Vintage' ]\n",
        "scaler = StandardScaler()\n",
        "trainData[numCols] = scaler.fit_transform(trainData[numCols])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "jMMf_6U26wvk"
      },
      "source": [
        "scalerM = MinMaxScaler()\n",
        "trainData[[\"Annual_Premium\"]] = scalerM.fit_transform(trainData[[\"Annual_Premium\"]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "WPQxQFIU6wvm"
      },
      "source": [
        "x = trainData.drop(['Response'], axis = 1)\n",
        "y = trainData['Response']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "zxBMI2WF6wvp"
      },
      "source": [
        "xTrain, xTest, yTrain, yTest = train_test_split(x,y,test_size = 0.4, random_state = 42 )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "uNNeG8xm6wvr",
        "outputId": "5e23b11c-3f14-49b3-e19e-1d11c64108d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 32
        }
      },
      "source": [
        "model = KNeighborsClassifier(n_neighbors = 11, weights = \"uniform\")\n",
        "model.fit(xTrain,yTrain)\n",
        "y_pred = model.predict(xTest)\n",
        "accuracy_score(yTest, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8669019443205374"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "dIAfxOII6wvw",
        "outputId": "8dfe4fbd-1ebd-4001-f691-387631ea45c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 32
        }
      },
      "source": [
        "roc_auc_score(yTest, y_pred, average = 'weighted')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5402572035112558"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    }
  ]
}